How Does AI Work?
Before we start, here are some key terms:
Artificial intelligence encompasses various approaches to mimic human intelligence, with machine learning gaining popularity. Machine learning involves inputting large datasets into a computer processor, enabling it to learn trends and make predictions on new data. Deep learning combines neural networks with machine learning to extract information from various sources.
In machine learning, features are measurable data characteristics used as inputs for prediction or classification, while targets represent desired results for informed decision-making. Supervised learning uses labeled training data to associate features with target labels, while unsupervised learning analyzes unlabeled data to discover patterns and relationships.
Building Blocks:
A diverse array of machine learning algorithms exists, all sharing a fundamental approach:
Analyzing a training dataset to discern patterns and relationships.
Applying clustering techniques, to identify patterns and quantify important features
Extracting relevant features and optimizing internal parameters to construct a predictive model that accurately captures underlying patterns.
Deploying the trained model to make accurate predictions on new, previously unseen data.
1. Raw Data
2. Clustering Data
3. Pattern Identification
4. Algorithm with Unseen Data
To aid visualization, a 2D representation is presented. However, this fundamental principle extends into infinite dimensions, allowing complex computing algorithms to offer unique insights beyond human capabilities.
Predictive analytical machine learning algorithms are invaluable tools in various fields, as they can efficiently analyze vast datasets to uncover hidden patterns and make precise predictions. Their ability to continuously learn from new data and adapt their models ensures their accuracy and reliability in providing actionable insights for decision-making.
Dimensionality:
Machine learning algorithms have the ability to comprehend and analyze data in higher dimensions than the human brain can easily visualize.
While humans typically excel at visualizing and reasoning in three dimensions, machine learning algorithms can effortlessly handle data with hundreds or even thousands of dimensions. By leveraging sophisticated mathematical techniques and algorithms, machine learning can navigate complex, high-dimensional spaces, uncovering insights and making predictions that surpass human cognitive capabilities.
In certain instances, like visualizing the distortion of space-time caused by gravity, employing gradients can offer a means to visually represent high-dimensional information within a lower-dimensional space. However, it is worth noting that this approach is seldom practical in most scenarios.
1-Dimension
2-Dimensions
3-Dimensions
4-Dimensions
As the number of dimensions in a model increases, the computational power required to process and analyze the data grows exponentially. This is because each additional dimension increases the complexity of calculations, memory storage, and distance calculations, resulting in a higher computational burden that demands more processing power and resources to handle the increased dimensional space efficiently.
How do we know if an algorithm is accurate?
An effective algorithm goes beyond data memorization and comprehends the relationships among data points, discerning patterns for reliable predictions. Models are fine-tuned by manipulating variables, determining mathematical relationships between data, and optimizing their configuration for ideal state.
Machine learning algorithms are assessed through various evaluation measures, including correctly classified instances, true positives, false positives, relevant instance retrieval, and discriminative power. Achieving optimal accuracy involves finding the balance between overfitting and underfitting, avoiding unnecessary complexity or oversimplification in the model.
Balanced Model
Underfitting (Model is too simple)
Overfitting (Model is too complex)
Types of Implementations:
Below are examples of how these foundational concepts are applied
Interpreting text:
Text vectorization converts textual data into numerical representations, aiding machine learning algorithms in processing and understanding text. It uses a distance metric called a "Euclidean-distance vector" to quantify relationships between data points in multi-dimensional space, facilitating tasks like text classification and sentiment analysis.
Vector embedding, or word embedding, represents words or phrases as numerical vectors in a high-dimensional space, capturing semantic relationships and contextual meanings for improved performance in natural language processing tasks.
Vectorization can also combine numerical data, creating a comprehensive representation that leverages both text and numerical features for effective analysis and predictions. Machine learning algorithms excel in representing intricate word relationships in a significantly higher dimensional space, enhancing understanding of underlying patterns and associations.
Euclidean-distance vector
Semantic distance
Neural Networks:
Neural networks have gained significant popularity due to their exceptional capability to analyze unstructured data, such as online content and text.
They simulate the architecture of the human brain by creating artificial neurons that produce binary signals when certain conditions are met. By constructing large configurations of these neurons, neural networks can comprehend intricate inputs and generate complex outputs.
A notable advantage of neural networks is their ability to classify images and text without explicit instructions, known as data clustering. This allows them to organize unstructured data by identifying features and outcomes without predefined labels, unlocking valuable insights and facilitating advanced analytics.
Artificial Neurons
Neural Network
Before Neural Network
After Neural Network
Regression Analysis
Linear regression and polynomial regression are powerful analytical techniques used to model relationships between dependent and independent variables in a dataset. Both regression methods find wide practical applications in economics, finance, marketing, medicine, environmental science, and education, offering valuable insights for data-driven decision-making processes.
Linear Regression: Fits a straight line to data points for prediction and models simple, linear relationships between variables.
Polynomial Regression: Fits a curve to data points, capturing non-linear relationships and modeling complex, non-linear patterns in the data.
Optimizing the polynomial order for the best results requires advanced machine learning techniques. The algorithm iteratively explores various possibilities to determine the optimal approximation, effectively capturing intricate relationships within the data.
Machine learning algorithms surpass traditional regression methods by offering greater flexibility in handling complex and non-linear relationships between variables, enabling more accurate representation of real-world data and improved prediction accuracy. Additionally, their ability to automate parameter optimization, adapt to new data, and handle large datasets further enhances their suitability for various practical applications.
2D Linear Regression
Higher Dimension Polynomial Regression
Decision trees
Decision trees are popular and intuitive machine learning algorithms for classification and regression tasks. They recursively split data based on features, creating a tree-like structure where internal nodes represent features, and leaf nodes represent classes or predicted values. Splitting criteria like Gini impurity or information gain guide the process to maximize subset homogeneity. Decision trees are interpretable, handling categorical and numerical features, capturing non-linear relationships.
Random forests are an ensemble method building on decision trees. They combine multiple trees through bagging, training on different data subsets. Trees in the forest operate independently, and predictions aggregate through majority voting or averaging for classification or regression. Random forests reduce overfitting, improve generalization, and handle noise and outliers robustly. They also estimate feature importance, aiding in understanding feature relevance for predictions.
Model Boosting
In the realm of machine learning, decision trees present various approaches, each with unique benefits and limitations that impact accuracy. Moreover, by combining multiple weak models, a process known as model boosting, a powerful ensemble model can be created, leading to improved predictive performance. Model boosting methods are a family of machine learning techniques that aim to improve the predictive performance of weak learners by combining them into a strong ensemble model.
The underlying principle of boosting is to iteratively train weak models in a sequential manner, where each subsequent model focuses on correcting the mistakes or misclassifications made by the previous models. Boosting methods, including AdaBoost and Gradient Boosting, have proven to be effective in various machine learning tasks, providing improved predictive accuracy and handling complex relationships in the data. They excel in scenarios where the weak learners are simple and may not perform well individually but can contribute collectively to a more accurate and robust model.